With the development of facial identification technology, user demands for identification of facial attributes continue to increase, such as facial age identification, facial gender identification and identification of a facial-happiness degree. For example, facial age identification helps to collect information of users of different age groups for analysis of popularity of certain products among different age groups.
Before identifying a facial age, multiple image samples are often acquired and the image samples are classified into several age groups. For a particular age group, the image samples that fall into the age group are usually taken as positive samples and the image samples that belong to other age groups are taken as negative samples. Training can be performed according to the positive samples and the negative samples to generate a classification function corresponding to each age group. Such a classification function can be used to estimate an age group for certain later-acquired facial images.
The above-noted conventional approach has certain problems. For example, the number of negative samples for a certain age group is over ten times the number of positive samples for the age group. Due to the uneven numbers of the positive samples and the negative samples for the age group, the positive samples and the negative samples often cannot be accurately selected during training and hence the classification function acquired from the training is usually inaccurate. Such an inaccurate classification function may not be used to accurately identify facial age, and therefore often causes a high error rate.
Hence it is highly desirable to improve the techniques for facial age identification.